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README.md

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-library_name: transformers
-tags: []
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-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
+# CodeGenDetect-CodeBERT
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+**Model Name:** `azherali/CodeGenDetect-CodeBert`  
+**Task:** Code Generation Detection (Human vs Machine Generated Code)  
+**Languages Supported:** C++, Java, Python  
+**Base Model:** CodeBERT  
+**Author:** Azher Ali  
 
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+---
 
-## Environmental Impact
+## 📌 Model Overview
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+`CodeGenDetect-CodeBert` is a transformer-based classification model designed to distinguish **human-written code** from **machine-generated code** produced by Large Language Models (LLMs). The model is fine-tuned on multilingual source code data spanning **C++**, **Java**, and **Python**, making it suitable for real-world, cross-language code analysis tasks.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Built on top of **CodeBERT**, the model leverages contextual and structural representations of source code to capture subtle stylistic, syntactic, and semantic patterns that differentiate human-authored code from AI-generated code.
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+---
 
-## Technical Specifications [optional]
+## 🎯 Intended Use Cases
 
-### Model Architecture and Objective
+This model is well-suited for:
 
-[More Information Needed]
+- **Academic integrity & plagiarism detection**
+- **LLM-generated code identification**
+- **Code authenticity verification**
+- **Research on AI-generated programming artifacts**
+- **Code forensics and auditing pipelines**
 
-### Compute Infrastructure
+---
 
-[More Information Needed]
+## 🧠 Model Details
 
-#### Hardware
+- **Architecture:** Transformer-based (CodeBERT)
+- **Task Type:** Binary Sequence Classification
+- **Labels:**
+  - `0` → Human-generated code
+  - `1` → Machine-generated (LLM) code
+- **Input:** Source code as plain text
+- **Output:** Class probabilities and predicted label
 
-[More Information Needed]
+---
 
-#### Software
+## 🌐 Supported Programming Languages
 
-[More Information Needed]
+The model has been trained and evaluated on code written in:
 
-## Citation [optional]
+- **C++**
+- **Java**
+- **Python**
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+It generalizes across these languages by learning language-agnostic code patterns while still capturing language-specific constructs.
 
-**BibTeX:**
+---
 
-[More Information Needed]
+## 🏋️ Training Summary
 
-**APA:**
+- **Training Objective:** Binary cross-entropy loss for classification
+- **Tokenization:** CodeBERT tokenizer with fixed-length padding and truncation
+- **Optimization:** Fine-tuned using modern deep learning best practices
+- **Evaluation Metrics:** Accuracy, Precision, Recall, F1-score
 
-[More Information Needed]
+The training data includes both human-written code and code generated by modern LLMs to ensure realistic detection performance.
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## 🚀 Example Usage
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-## More Information [optional]
+model_name = "azherali/CodeGenDetect-CodeBert"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-## Model Card Authors [optional]
+code_snippet = """
+def add(a, b):
+    return a + b
+"""
 
-[More Information Needed]
+inputs = tokenizer(code_snippet, return_tensors="pt", truncation=True, padding=True)
+outputs = model(**inputs)
 
-## Model Card Contact
+prediction = torch.argmax(outputs.logits, dim=1).item()
+label = "Machine-generated" if prediction == 1 else "Human-written"
 
-[More Information Needed]
+print(label)